gpsaggese · Utkrisht12 · May 7, 2026
diff --git a/tutorials/GluonTS_COVID19_Prediction/GluonTS.API.py b/tutorials/GluonTS_COVID19_Prediction/GluonTS.API.py
@@ -6,9 +6,9 @@
 #       extension: .py
 #       format_name: percent
 #       format_version: '1.3'
-#       jupytext_version: 1.19.0
+#       jupytext_version: 1.19.1
 #   kernelspec:
-#     display_name: Python 3
+#     display_name: Python 3 (ipykernel)
 #     language: python
 #     name: python3
 # ---
@@ -54,25 +54,39 @@
 # Once you're comfortable with the mechanics here, move to `GluonTS.example.ipynb` to see these models applied to real COVID-19 data.
 
 # %% [markdown]
-# ---
-#
 # ## Setup
 
 # %%
+# %load_ext autoreload
+# %autoreload 2
+
+# System libraries.
+import logging
 import warnings
 
 warnings.filterwarnings("ignore")
 
-# Core GluonTS components for the API tutorial
+# Third party libraries.
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import seaborn as sns
+
+# %% [markdown]
+# ## GluonTS imports and utilities
+
+# %%
+from gluonts.evaluation import make_evaluation_predictions
+from gluonts.torch.model.deep_npts import DeepNPTSEstimator
 from gluonts.torch.model.deepar import DeepAREstimator
 from gluonts.torch.model.simple_feedforward import SimpleFeedForwardEstimator
-from gluonts.torch.model.deep_npts import DeepNPTSEstimator
-from gluonts.evaluation import make_evaluation_predictions
 
-# All our utilities are in one place - much cleaner!
 import GluonTS_utils as gluonts
 
-print("Setup complete. Ready to explore GluonTS.")
+# %%
+_LOG = logging.getLogger(__name__)
+gluonts.init_logger(_LOG)
+_LOG.info("Setup complete. Ready to explore GluonTS.")
 
 # %% [markdown]
 # ## The GluonTS Workflow
@@ -90,8 +104,6 @@
 # Let's see this in action, starting with the simplest possible time series.
 
 # %% [markdown]
-# ---
-#
 # # Level 1: Sinusoid — The Simplest Pattern
 #
 # We begin with a pure sine wave plus a small amount of Gaussian noise. This is the easiest pattern a model can encounter: perfectly periodic, no trend, no regime changes.
@@ -245,8 +257,6 @@
 # %% [markdown]
 # > **Checkpoint:** You just completed the full GluonTS workflow — configure, train, forecast, visualize, evaluate. This is the same 5-step pattern for *every* GluonTS model. From here on we'll move faster since you know the drill. What changes is the **data** (increasing complexity) and the **model** (different architectures), not the workflow itself.
 #
-# ---
-#
 # # Level 2: Multi-Frequency — Adding Realism
 #
 # Real time series rarely consist of a single clean cycle. This synthetic series combines four components you'll encounter in real data:
@@ -372,8 +382,6 @@
 )
 
 # %% [markdown]
-# ---
-#
 # # Level 3: Regime Change — The Hard Problem
 #
 # This is where things get interesting. The series behaves one way for the first half, then **abruptly shifts** to a different baseline, amplitude, and frequency.
@@ -506,15 +514,11 @@
 print(f"  MAPE: {deepar_regime_metrics['mape']:.2f}%")
 
 # %% [markdown]
-# ---
-#
 # # Model Comparison
 #
 # Let's bring all results together. Each model was tested on the data type that best highlights its strengths and weaknesses.
 
 # %%
-import pandas as pd
-
 comparison = pd.DataFrame(
     {
         "Model": [
@@ -551,8 +555,6 @@
 print(comparison.to_string(index=False, float_format="%.2f"))
 
 # %% [markdown]
-# ---
-#
 # # Summary
 #
 # ## What You Learned
@@ -563,8 +565,6 @@
 # 4. **Probabilistic output** — every forecast gives you means, medians, quantiles, and raw samples
 # 5. **Model choice matters** — the right model depends on your data's characteristics
 #
-# ---
-#
 # ## Which Model Should You Choose?
 #
 # | If your data has... | Try this model | Why |
@@ -574,8 +574,6 @@
 # | Regime shifts, unusual distributions | **DeepNPTS** | Non-parametric — adapts to changing behavior |
 # | No idea yet | **Start with SimpleFeedForward** | Fast to test, then try DeepAR for more accuracy |
 #
-# ---
-#
 # ## Quick Reference
 #
 # | Task | Code |
@@ -585,8 +583,6 @@
 # | 80% confidence interval | `forecast.quantile(0.1)` to `forecast.quantile(0.9)` |
 # | Raw sample paths | `forecast.samples` (shape: `num_samples × prediction_length`) |
 #
-# ---
-#
 # ## Tips for Better Results
 #
 # | Area | Tip |
@@ -596,8 +592,6 @@
 # | **Features** | More isn't always better — test with and without |
 # | **Data quality** | Handle missing values and normalize if needed |
 #
-# ---
-#
 # ## Troubleshooting
 #
 # | Problem | Fix |
@@ -607,14 +601,10 @@
 # | *Poor forecast quality* | Increase `context_length`, train longer, or try DeepNPTS for regime changes |
 # | *"Unexpected keyword argument"* | DeepAR: `trainer_kwargs={"max_epochs": N}`. DeepNPTS: `epochs=N`. SimpleFeedForward: no `freq` parameter |
 #
-# ---
-#
 # ## Resources
 #
 # - [GluonTS Documentation](https://ts.gluon.ai/) · [GitHub](https://github.com/awslabs/gluonts) · [PyTorch Lightning](https://lightning.ai/docs/pytorch/stable/)
 #
-# ---
-#
 # ## What's Next?
 #
 # Now that you understand how GluonTS works on clean synthetic data, move to **`GluonTS.example.ipynb`** to see these same models applied to real **COVID-19 case prediction** — with feature engineering, multiple covariates, scenario analysis, and all the messiness of real-world data.

diff --git a/tutorials/GluonTS_COVID19_Prediction/GluonTS.example.py b/tutorials/GluonTS_COVID19_Prediction/GluonTS.example.py
@@ -6,7 +6,7 @@
 #       extension: .py
 #       format_name: percent
 #       format_version: '1.3'
-#       jupytext_version: 1.19.0
+#       jupytext_version: 1.19.1
 #   kernelspec:
 #     display_name: Python 3 (ipykernel)
 #     language: python
@@ -25,21 +25,31 @@
 # **Models:** We compare DeepAR (complex patterns), SimpleFeedForward (fast baseline), and DeepNPTS (regime changes).
 
 # %% [markdown]
-# ---
-#
-# ## 1. Setup and Imports
+# ## 1. Setup and imports
 #
 # Let's get everything set up for our COVID-19 forecasting analysis.
 
 # %%
+# %load_ext autoreload
+# %autoreload 2
+
+# System libraries.
+import logging
 import warnings
 
 warnings.filterwarnings("ignore")
 
-# All our utilities in one place - much cleaner!
-import GluonTS_utils as gluonts
+# Third party libraries.
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import seaborn as sns
 
-# Explicit imports for functions called without gluonts. prefix
+# %% [markdown]
+# ## GluonTS utilities
+
+# %%
+import GluonTS_utils as gluonts
 from GluonTS_utils import (
     train_deepar_covid,
     train_feedforward_covid,
@@ -51,11 +61,12 @@
     print_policy_insights,
 )
 
-print("Setup complete. Ready to forecast COVID-19 cases.")
+# %%
+_LOG = logging.getLogger(__name__)
+gluonts.init_logger(_LOG)
+_LOG.info("Setup complete. Ready to forecast COVID-19 cases.")
 
 # %% [markdown]
-# ---
-#
 # ## 2. Load and Explore COVID-19 Data
 #
 # Let's load our real COVID-19 data and take a look at what we're working with.
@@ -116,8 +127,6 @@
 # **Why these features?** Target (7-day MA) smooths reporting; deaths lag cases and correlate with outcomes; CFR indicates strain; mobility captures lockdown effects.
 
 # %% [markdown]
-# ---
-#
 # ## 3. Feature Engineering
 #
 # Our data pipeline has already engineered several features to improve model performance:
@@ -148,8 +157,6 @@
 )
 
 # %% [markdown]
-# ---
-#
 # ## 4. Train All Three Models
 #
 # **Model choice:** DeepAR for complex wave patterns; SimpleFeedForward for a fast baseline; DeepNPTS for regime shifts across COVID variants.
@@ -230,8 +237,6 @@
 )
 
 # %% [markdown]
-# ---
-#
 # ## 5. Compare Models
 #
 # Now that we've trained all three models, let's compare their performance!
@@ -293,8 +298,6 @@
 )
 
 # %% [markdown]
-# ---
-#
 # ## 6. Scenario Analysis: Simulating Interventions
 #
 # One of the most powerful applications of forecasting is **scenario analysis** -
@@ -372,8 +375,6 @@
 print_policy_insights(scenario_results)
 
 # %% [markdown]
-# ---
-#
 # ## 7. Conclusions and Recommendations
 #
 # ### Key Takeaways
@@ -438,8 +439,6 @@
 # 4. **Scalability**: Use GPU acceleration for faster training
 # 5. **Interpretability**: Provide explanations alongside forecasts
 #
-# ---
-#
 # ## Congratulations!
 #
 # You've completed a full end-to-end COVID-19 forecasting application!
@@ -455,8 +454,6 @@
 # **Ready to apply these skills to your own forecasting problems?**
 
 # %% [markdown]
-# ---
-#
 # ## Additional Resources
 #
 # **GluonTS Documentation**

diff --git a/tutorials/GluonTS_COVID19_Prediction/GluonTS_utils.py b/tutorials/GluonTS_COVID19_Prediction/GluonTS_utils.py
@@ -70,6 +70,33 @@
 warnings.filterwarnings("ignore")
 
 
+def init_logger(notebook_log: logging.Logger) -> None:
+    """
+    Configure notebook display and route loggers to print for Jupyter output.
+
+    Standalone tutorial images do not include the helpers package, so this
+    mirrors the essentials of helpers.hnotebook.config_notebook locally.
+    """
+    import seaborn as sns
+
+    plt.rcParams["figure.figsize"] = (12, 6)
+    plt.rcParams["legend.fontsize"] = 12
+    plt.rcParams["font.size"] = 12
+    pd.set_option("display.max_rows", 500)
+    pd.set_option("display.max_columns", 500)
+    pd.set_option("display.width", 1000)
+    sns.set()
+
+    def _info_print(msg: str, *args, **kwargs) -> None:
+        if args:
+            msg = msg % args
+        print(msg)
+
+    notebook_log.info = _info_print  # type: ignore[method-assign]
+    global _LOG
+    _LOG.info = _info_print  # type: ignore[method-assign]
+
+
 # #############################################################################
 # Analysis
 # #############################################################################